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10.1.1.4.2297 - 1 A Robust Reputation System for Mobile...

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1 A Robust Reputation System for Mobile Ad-hoc Networks EPFL IC Technical Report IC/2003/50 Sonja Buchegger EPFL-IC-LCA CH-1015 Lausanne, Switzerland [email protected] Jean-Yves Le Boudec EPFL-IC-LCA CH-1015 Lausanne, Switzerland [email protected] Abstract —Reputation systems in mobile ad-hoc networks can be tricked by the spreading of false reputation ratings, be it false accusations or false praise. Simple solutions such as exclusively relying on one’s own direct observations have drawbacks, as they do not make use of all the information available. We propose a fully distributed reputation sys- tem that can cope with false disseminated information. In our approach, everyone maintains a reputation rating and a trust rating about everyone else that they care about. From time to time first-hand reputation information is exchanged with others; using a modified Bayesian approach we de- signed and present in this paper, only second-hand repu- tation information that is not incompatible with the current reputation rating is accepted. Thus, reputation ratings are slightly modified by accepted information. Trust ratings are updated based on the compatibility of second-hand reputa- tion information with prior reputation ratings. Data is en- tirely distributed: someone’s reputation and trust is the col- lection of ratings maintained by others. We enable node re- demption and prevent the sudden exploitation of good repu- tation built over time by introducing re-evaluation and rep- utation fading. We present the application of our generic reputation system to the context of neighborhood watch in mobile ad-hoc networks, specifically to the CONFIDANT [3] protocol for the detection and isolation of nodes exhibit- ing routing or forwarding misbehavior. We evaluate the performance by simulation. Index Terms —System design, Simulations, Statistics I. I NTRODUCTION A. Motivation Reputation systems have been proposed for a variety of applications, among them are the selection of good peers in a peer-to-peer network, the choice of transac- tion partners for online auctioning, and the detection of misbehaved nodes in mobile ad-hoc networks. There is a trade-off between efficiency in using the available in- formation and robustness against false ratings [4]. If the ratings made by others are considered, the reputation sys- tem can be vulnerable to false accusations or false praise. However, if only one’s own experience is considered, the potential of learning from experience made by others goes unused. Using only positive or only negative information reduces the vulnerability to only false praise or only false accusations. Our goal is to make neighborhood watch systems both robust against false ratings and efficient at detecting mis- behavior. We propose a mechanism that makes use of all the available information, i.e. both positive and negative, both from own and from others’ experience. To make the reputation system robust we include a way of dealing with false ratings.
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